Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA.
Ann Surg Oncol. 2023 Aug;30(8):5215-5224. doi: 10.1245/s10434-023-13192-1. Epub 2023 Mar 1.
The validation of breast cancer risk biomarkers in benign breast samples (BBS) is a long-sought goal, hampered by the fluctuation of gene and protein expression with menstrual phase (MP) and menopausal status (MS). Previously, we identified hormone-related gene expression and histomorphology parameters to classify BBS by MS/MP. We now evaluate both together, to validate our prior results.
BBS were obtained from consenting women (86 premenopausal, 55 postmenopausal) undergoing reduction mammoplasty (RM) or contralateral unaffected breast (CUB) mastectomy. MP/MS was defined using classical criteria for menstrual dates and hormone levels on the day of surgery. BBS gene expression was measured with reverse transcription quantitative polymerase chain reaction (RT-qPCR) for three luteal phase (LP) genes (TNFSF11, DIO2, MYBPC1) and four menopausal genes (PGR, GREB1, TIFF1, CCND1). Premenopausal samples were classified into LP or non-LP, using published histomorphology parameters. Logistic regression and receiver-operator curve analysis was performed to assess area under the curve (AUC) for prediction of MP/MS.
In all 131 women, menopausal genes plus age > 50 years predicted true MS [AUC 0.93, 95% confidence interval (CI) 0.89, 0.97]. Among premenopausal women, high TNFSF11 expression distinguished non-LP from LP samples (AUC 0.80, 95% CI 0.70, 0.91); the addition of histomorphology improved the prediction nonsignificantly (AUC 0.87, 95% CI 0.78, 0.96). In premenopausal subsets, addition of histomorphology improved LP prediction in RM (AUC 0.95, 95% CI 0.87, 1.0), but not in CUB (0.84, 95% CI 0.72, 0.96).
Expression of five-gene set accurately predicts menopausal status and menstrual phase in BBS, facilitating the development of breast cancer risk biomarkers using large, archived sample repositories.
在良性乳腺样本(BBS)中验证乳腺癌风险生物标志物是一个长期以来的目标,但由于基因和蛋白质表达随月经周期(MP)和绝经状态(MS)而波动,这一目标一直难以实现。此前,我们已经确定了与激素相关的基因表达和组织形态学参数,以便根据 MS/MP 对 BBS 进行分类。现在,我们同时评估这两个因素,以验证我们之前的结果。
本研究纳入了 86 例接受缩乳术(RM)或对侧未受影响乳房(CUB)乳房切除术的绝经前和 55 例绝经后女性,她们均签署了同意书。MP/MS 是根据月经日期和手术当天的激素水平的经典标准来定义的。通过逆转录定量聚合酶链反应(RT-qPCR)测量 BBS 的三个黄体期(LP)基因(TNFSF11、DIO2、MYBPC1)和四个绝经基因(PGR、GREB1、TIFF1、CCND1)的基因表达。使用已发表的组织形态学参数,将绝经前样本分为 LP 或非 LP。进行逻辑回归和接收者操作特征曲线分析,以评估曲线下面积(AUC)在预测 MP/MS 中的作用。
在所有 131 名女性中,绝经基因加年龄>50 岁可以准确预测真实的 MS[AUC 0.93,95%置信区间(CI)0.89,0.97]。在绝经前女性中,高 TNFSF11 表达可将非 LP 与 LP 样本区分开来(AUC 0.80,95%CI 0.70,0.91);添加组织形态学可略微改善预测结果(AUC 0.87,95%CI 0.78,0.96)。在绝经前亚组中,添加组织形态学可改善 RM 中 LP 预测的准确性(AUC 0.95,95%CI 0.87,1.0),但在 CUB 中则不能(0.84,95%CI 0.72,0.96)。
五基因集的表达可准确预测 BBS 的绝经状态和月经周期,这为利用大型存档样本库开发乳腺癌风险生物标志物提供了便利。